Dynamic Medical Image Compression Preserving Diagnostic Data
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Solution Overview
Problem
Current digital image compression techniques in medical imaging often result in the loss of significant information, which can lead to misdiagnoses due to inadequate selection methods and varying implementation standards, particularly in fields where quantitative measurements are crucial.
Innovation Solution
A system dynamically selects appropriate compression techniques and parameters based on image characteristics, patient characteristics, and medical history, using compression rules to maintain significant information and switch to lossless compression when necessary, allowing for segmented compression across different image portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression is applied to reduce file size, then storage space requirements are reduced, but significant information may be lost
Solution Approach 1:
The system dynamically changes compression parameters (compression ratio, quality factor) based on image characteristics and clinical requirements. Different compression parameters are selected for different regions of the image, allowing aggressive compression in non-critical areas while maintaining quality in diagnostically important regions.
Solution Approach 2:
The patent applies different compression quality levels to different regions of the same image. Regions containing diagnostically significant information (such as lesions or abnormalities) are compressed with higher quality settings, while non-critical regions use more aggressive compression, thus preserving significant information overall while reducing total file size.
2Quantity of substance
If lossy compression is used to reduce storage and bandwidth requirements, then storage cost and transfer time are reduced, but diagnostic accuracy may be compromised
Solution Approach 1:
Compression parameters are dynamically adjusted based on the specific clinical context, patient history, and image characteristics. The system selects from multiple compression algorithms and parameter sets to achieve the highest possible compression while maintaining diagnostic accuracy for the given clinical scenario.
Solution Approach 2:
The compression system is dynamic rather than static. It automatically adapts compression settings based on real-time analysis of image content, clinical indications, and diagnostic requirements. This allows the system to optimize the balance between compression ratio and diagnostic accuracy for each specific case.
3Productivity
If fixed compression parameters are used, then processing speed is improved, but adaptability to different image characteristics is reduced
Solution Approach 1:
The system employs dynamic parameter selection where compression settings are automatically adjusted based on image characteristics, clinical context, and diagnostic requirements. Multiple pre-configured parameter sets are available, and the system dynamically selects the most appropriate one for each specific case, balancing speed and effectiveness.
Solution Approach 2:
The system performs preliminary analysis of image characteristics and clinical requirements before applying compression. This pre-processing step identifies diagnostically important regions and determines appropriate compression parameters in advance, allowing the actual compression to proceed efficiently with optimized settings already determined.
4Quantity of substance
If aggressive compression is applied to maximize file size reduction, then storage efficiency is improved, but loss of clinically significant information increases
Solution Approach 1:
The patent implements region-specific compression where diagnostically important areas (such as regions containing lesions, abnormalities, or anatomical structures of interest) are preserved with high quality, while non-critical background areas undergo more aggressive compression. This selective approach maximizes storage efficiency while protecting clinically significant information.
Solution Approach 2:
The system incorporates feedback mechanisms where compression results are evaluated against quality thresholds and clinical requirements. If compression causes loss of significant information, the system adjusts parameters and re-compresses, ensuring that clinically relevant data is preserved while achieving maximum feasible compression.
Data Source
AI summary
Systems and techniques are disclosed for dynamically and automatically selecting an appropriate compression technique and/or compression parameters for digital images in order to reduce or prevent loss of significant information that may negatively impact the utility or usefulness of the digital images. For example, based on various image characteristics associated with a digital image, the system may dynamically compress the image using particular compression techniques and/or by adjusting compression parameters, to maintain significant information of the image. The system may select compression techniques and/or compression parameters based on one or more compression rules, which may be associated with image characteristics, patient characteristics, medical history, etc. Further, the system may, based on the one or more compression rules, compress the image to a maximum degree of compression while maintaining the significant information of the image.